#!/usr/bin/env python
from math import sqrt

# A dictionary of movie critics and their ratings of a small
# set of movies
critics = {
    'Lisa Rose': {
        'Lady in the Water': 2.5,
        'Snakes on a Plane': 3.5,
        'Just My Luck': 3.0,
        'Superman Returns': 3.5,
        'You, Me and Dupree': 2.5,
        'The Night Listener': 3.0
    },
    'Gene Seymour': {
        'Lady in the Water': 3.0,
        'Snakes on a Plane': 3.5,
        'Just My Luck': 1.5,
        'Superman Returns': 5.0,
        'The Night Listener': 3.0,
        'You, Me and Dupree': 3.5
    },
    'Michael Phillips': {
        'Lady in the Water': 2.5,
        'Snakes on a Plane': 3.0,
        'Superman Returns': 3.5,
        'The Night Listener': 4.0
    },
    'Claudia Puig': {
        'Snakes on a Plane': 3.5,
        'Just My Luck': 3.0,
        'The Night Listener': 4.5,
        'Superman Returns': 4.0,
        'You, Me and Dupree': 2.5
    },
    'Mick LaSalle': {
        'Lady in the Water': 3.0,
        'Snakes on a Plane': 4.0,
        'Just My Luck': 2.0,
        'Superman Returns': 3.0,
        'The Night Listener': 3.0,
        'You, Me and Dupree': 2.0
    },
    'Jack Matthews': {
        'Lady in the Water': 3.0,
        'Snakes on a Plane': 4.0,
        'The Night Listener': 3.0,
        'Superman Returns': 5.0,
        'You, Me and Dupree': 3.5
    },
    'Toby': {
        'Snakes on a Plane': 4.5,
        'You, Me and Dupree': 1.0,
        'Superman Returns': 4.0
    }
}



def sim_distance(prefs, person1, person2):
    si = {}
    for item in prefs[person1]:
        if item in prefs[person2]:
            si[item] = 1

    if len(si) == 0: return 0

    sum_of_squares = sum([pow(prefs[person1][item]-prefs[person2][item],2)
                          for item in prefs[person1] if item in prefs[person2]])

    return 1/(1+sqrt(sum_of_squares))


def sim_pearson(prefs, p1, p2):
    si = {}

    for item in prefs[p1]:
        if item in prefs[p2]: si[item] = 1
        
    n = len(si)

    if n == 0: return 1

    sum1 = sum([prefs[p1][it] for it in si])
    sum2 = sum([prefs[p2][it] for it in si])

    sum1Sq = sum([pow(prefs[p1][it],2) for it in si])
    sum2Sq = sum([pow(prefs[p2][it],2) for it in si])

    pSum = sum([prefs[p1][it]*prefs[p2][it] for it in si])

    num = pSum-(sum1*sum2/n)
    den = sqrt((sum1Sq-pow(sum1, 2)/n) *(sum2Sq-pow(sum2, 2)/n))

    if den ==0: return 0

    r = num /den

    return r

def topMatches(prefs, person, n= 5, similarity=sim_pearson):
    scores = [(similarity(prefs, person, other), other) for other in prefs if other != person]

    scores.sort()
    scores.reverse()
    return scores



like_value = sim_distance(critics, 'Lisa Rose', 'Gene Seymour')
like_value_LT = sim_distance(critics, 'Lisa Rose', 'Toby')

print(like_value)
print(like_value_LT)

like_rec = sim_pearson(critics, 'Lisa Rose', 'Gene Seymour')
print(like_rec)


like_list = topMatches(critics, 'Toby', n=3)
print(like_list)